rate_posterior: remove commented out code

- a bunch of code was added related to the alphabet soup approach, and then
  commented out.  this patch removes it.  it can be reverted later if
  somebody wants to resurrect it

gstlal-inspiral/bin/gstlal_inspiral_rate_posterior

@@ -293,7 +293,6 @@ if options.chain_file is not None:
 if options.samples is not None:
 	kwargs["nsample"] = options.samples
 signal_rate_pdf, noise_rate_pdf = rate_estimation.calculate_rate_posteriors(rankingstatpdf, zerolag_ln_likelihood_ratios, progressbar = progressbar, **kwargs)
-#signal_rate_pdf, signal2_rate_pdf, signal12_rate_pdf, noise_rate_pdf = rate_estimation.calculate_alphabetsoup_rate_posteriors(rankingstatpdf, zerolag_ln_likelihood_ratios, progressbar = progressbar, **kwargs)
 #p_signal = rate_estimation.calculate_psignal_posteriors_from_rate_samples(rankingstatpdf, zerolag_ln_likelihood_ratios, progressbar = progressbar)
 #while open("p_signal.txt", "w") as f:
 #	for vals in zip(zerolag_ln_likelihood_ratios, p_signal):
@@ -310,29 +309,15 @@ if options.credible_intervals:
 	if options.verbose:
 		print >>sys.stderr, "determining credible intervals ..."
 	credible_intervals = dict((cred, rate_estimation.confidence_interval_from_binnedarray(signal_rate_pdf, cred)) for cred in options.credible_intervals)
-	#credible_intervals2 = dict((cred, rate_estimation.confidence_interval_from_binnedarray(signal2_rate_pdf, cred)) for cred in options.credible_intervals)
-	#credible_intervals12 = dict((cred, rate_estimation.confidence_interval_from_binnedarray(signal12_rate_pdf, cred)) for cred in options.credible_intervals)
 else:
 	credible_intervals = None
-	#credible_intervals2 = None
-	#credible_intervals12 = None
 if options.verbose and credible_intervals is not None:
 	print >>sys.stderr, "rate posterior mean = %g signals/experiment" % rate_estimation.mean_from_pdf(signal_rate_pdf)
 	print >>sys.stderr, "rate posterior median = %g signals/experiment" % rate_estimation.median_from_pdf(signal_rate_pdf)
-	#print >>sys.stderr, "rate 2 posterior mean = %g signals/experiment" % rate_estimation.mean_from_pdf(signal2_rate_pdf)
-	#print >>sys.stderr, "rate 2 posterior median = %g signals/experiment" % rate_estimation.median_from_pdf(signal2_rate_pdf)
-	#print >>sys.stderr, "rate 1+2 posterior mean = %g signals/experiment" % rate_estimation.mean_from_pdf(signal12_rate_pdf)
-	#print >>sys.stderr, "rate 1+2 posterior median = %g signals/experiment" % rate_estimation.median_from_pdf(signal12_rate_pdf)
 	# all modes are the same, pick one and report it
 	print >>sys.stderr, "maximum-likelihood rate = %g signals/experiment" % credible_intervals.values()[0][0]
-	#print >>sys.stderr, "maximum-likelihood rate 2 = %g signals/experiment" % credible_intervals2.values()[0][0]
-	#print >>sys.stderr, "maximum-likelihood rate 1+2 = %g signals/experiment" % credible_intervals12.values()[0][0]
 	for cred, (mode, lo, hi) in sorted(credible_intervals.items()):
 		print >>sys.stderr, "%g%% credible interval = [%g, %g] signals/experiment" % (cred * 100., lo, hi)
-	#for cred, (mode, lo, hi) in sorted(credible_intervals2.items()):
-	#	print >>sys.stderr, "%g%% credible interval 2 = [%g, %g] signals/experiment" % (cred * 100., lo, hi)
-	#for cred, (mode, lo, hi) in sorted(credible_intervals12.items()):
-	#	print >>sys.stderr, "%g%% credible interval 1+2 = [%g, %g] signals/experiment" % (cred * 100., lo, hi)
 
 
 #